Genetic programming (GP) is not a field noted for the rigor of its benchmarking. Some of its benchmark problems are popular purely through historical contingency, and they can be criticized as too easy or as providing misleading information concerning real-world performance, but they persist largely because of inertia and the lack of good alternatives. Even where the problems themselves are impeccable, comparisons between studies are made more difficult by the lack of standardization. We argue that the definition of standard benchmarks is an essential step in the maturation of the field. We make several contributions towards this goal. We motivate the development of a benchmark suite and define its goals; we survey existing practice; we enumerate many candidate benchmarks; we report progress on reference implementations; and we set out a concrete plan for gathering feedback from the GP community that would, if adopted, lead to a standard set of benchmarks.
We present the results of a community survey regarding genetic programming benchmark practices. Analysis shows broad consensus that improvement is needed in problem selection and experimental rigor. While views expressed in the survey dissuade us from proposing a large-scale benchmark suite, we find community support for creating a ''blacklist'' of problems which are in common use but have important flaws, and whose use should therefore be discouraged. We propose a set of possible replacement problems.
Intrusion detection for computer network systems becomes one of the most critical tasks for network administrators today. It has an important role for organizations, governments and our society due to its valuable resources on computer networks. Traditional misuse detection strategies are unable to detect new and unknown intrusion. Besides, anomaly detection in network security is aim to distinguish between illegal or malicious events and normal behavior of network systems. Anomaly detection can be considered as a classification problem where it builds models of normal network behavior, which it uses to detect new patterns that significantly deviate from the model. Most of the current research on anomaly detection is based on the learning of normally and anomaly behaviors. They do not take into account the previous, recent events to detect the new incoming one. In this paper, we propose a real time collective anomaly detection model based on neural network learning and feature operating. Normally a Long Short-Term Memory Recurrent Neural Network (LSTM RNN) is trained only on normal data and it is capable of predicting several time steps ahead of an input. In our approach, a LSTM RNN is trained with normal time series data before performing a live prediction for each time step. Instead of considering each time step separately, the observation of prediction errors from a certain number of time steps is now proposed as a new idea for detecting collective anomalies. The prediction errors from a number of the latest time steps above a threshold will indicate a collective anomaly. The model is built on a time series version of the KDD 1999 dataset. The experiments demonstrate that it is possible to offer reliable and efficient for collective anomaly detection.
This paper proposes latent representation models for improving network anomaly detection. Well-known anomaly detection algorithms often suffer from challenges posed by network data, such as high dimension and sparsity, and a lack of anomaly data for training, model selection, and hyperparameter tuning. Our approach is to introduce new regularizers to a classical autoencoder (AE) and a variational AE, which force normal data into a very tight area centered at the origin in the nonsaturating area of the bottleneck unit activations. These trained AEs on normal data will push normal points toward the origin, whereas anomalies, which differ from normal data, will be put far away from the normal region. The models are very different from common regularized AEs, sparse AE, and contractive AE, in which the regularized AEs tend to make their latent representation less sensitive to changes of the input data. The bottleneck feature space is now used as a new data representation. A number of one-class learning algorithms are used for evaluating the proposed models. The experiments testify that our models help these classifiers to perform efficiently and consistently on high-dimensional and sparse network datasets, even with relatively few training points. More importantly, the models can minimize the effect of model selection on these classifiers since their performance is insensitive to a wide range of hyperparameter settings.
Publication informationInternational Journal of Design Engineering, 3 (1): 4-24 Publisher Inderscience EnterprisesLink to online version http://dx. Martin Hemberg is a post-doctoral researcher at the Department of Ophthalmology at Children's Hospital Boston. He obtained is PhD from Imperial College London and he has also worked at the Architectural Association in London. His primary research interests include matheEvolutionary design using grammatical evolution and shape grammars 3 matical and computational models of gene expression
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